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首页> 外文会议>2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining >Application of Gaussian Process Regression for bearing degradation assessment
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Application of Gaussian Process Regression for bearing degradation assessment

机译：高斯过程回归在轴承退化评估中的应用

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摘要

Life prediction of bearing is the urgent demand in engineering practice, and the effective bearing degradation assessment technique is beneficial to predictive maintenance. This paper presents an application of an important Bayesian machine learning method named Gaussian Process Regression (GPR) for bearing degradation assessment. The Gaussian Process (GP) model holds many advantages such as easy coding, prediction with probability interpretation and self-adaptive acquisition of hyper-parameters. In this study, the GPR model with different kinds of covariance functions is applied for assessment of bearing state of health (SOH). Two common covariance functions and a composite covariance function of GPR which is obtained by additive single standard covariance functions are discussed. The dynamic model is introduced to realize a better assessment by analyzing some important features. From the experimental results, it can be concluded that using GPR model for prognosis can achieve a high performance, and the composite covariance function can improve the prediction precision. In addition, compared with wavelet neural network (WNN), GPR model shows more excellent features. So the purposed model can be utilized in bearing degradation analysis, and meanwhile can serve as a reference for similar data-mining projects.
机译：轴承的寿命预测是工程实践中的迫切需求，有效的轴承退化评估技术有利于预测性维护。本文介绍了一种重要的贝叶斯机器学习方法，即高斯过程回归（GPR），用于轴承退化评估。高斯过程（GP）模型具有许多优点，例如易于编码，具有概率解释的预测以及超参数的自适应获取。在这项研究中，将具有不同协方差函数的GPR模型用于评估健康状况（SOH）。讨论了两个常见的协方差函数和通过加法单一标准协方差函数获得的GPR的复合协方差函数。引入动态模型以通过分析一些重要特征来实现更好的评估。从实验结果可以得出结论，使用GPR模型进行预后可以达到较高的性能，而复合协方差函数可以提高预测精度。此外，与小波神经网络（WNN）相比，GPR模型具有更出色的功能。因此，该模型可用于轴承退化分析，同时可为类似的数据挖掘项目提供参考。

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